Distributed Ledger Technologies (DLTs) and right to health: the impact of blockchain in the healthcare sector

The aim of this contribution is to understand whether and how the blockchain technology can be used in the health sector, analyzing the ways in which its incorporation into the management of health data infrastructure might have the potential to create a more efficient national healthcare system. L’obiettivo del presente contributo è quello di analizzare se e come la tecnologia blockchain possa essere utilizzata nel settore sanitario, verificando le modalità in cui la sua incorporazione nella gestione dell’infrastruttura dei dati sanitari potrebbe avere il potenziale per creare un sistema sanitario nazionale più efficiente

Перспективи технології блокчейн в медичний сфері

В останнє десятиліття блокчейн стає однією з найперспективніших технологій, яка привертає увагу академічних досліджень і різноманітних сфер промисловості. Блокчейн — однорангова мережа, в якій фіксуються транзакції, і незмінність якої гарантується сукупністю користувачів (комп’ютерів), без участі централізованого органу. Мережа блокчейн складається з упорядкованих записів, організованих у блочну структуру. Кожен блок даних містить хеш (унікальний ідентифікатор), пакети останніх транзакцій із мітками часу та хеш попереднього блоку. Тож власне ланцюг, в якому кожен попередній блок з’єднаний у хронологічному порядку з наступним, і називають блокчейном

Design of a Healthcare Monitoring and Communication System for Locked-In Patients Using Machine Learning, IOTs, and Brain-Computer Interface Technologies

Machine learning (ML) models have shown great promise in advancing brain-computer interface (BCI) signal processing and in enhancing the capabilities of Internet of Things (IoT) mobile devices. By combining these advancements into a comprehensive healthcare monitoring and communication system, we may significantly improve the quality of life for patients living with locked-in syndrome. To that effect, we present a three-tiered approach to systems design using known ML models: data collection, local integrated system deployed on IoT hardware, and administrative management. The first tier focuses on IoT sensors and non-invasive recording of brain signals, their calibration and data collection, and data processing. The second tier focuses on aggregating and directing the data, an alert system for caregivers, and a BCI for personalized communication. The last tier focuses on accountability and essential management tools. This research-in-progress demonstrates the feasibility of integrating current technologies to improve care for locked-in patients

Critical issues in Leveraging Blockchain in Healthcare Sector

Blockchain innovation has brought various benefits to the healthcare sector. Utilizing blockchains in clinical contexts will reduce handling time since when a patient signs up for a review, the complete collected data will be accessible at once because of accessibility on the distributed ledger. Also, specialists will not need to stress over the patients giving them a legit clinical history, because of their capacity to progressively see the correct, credible, and quality source-recorded information. It eliminates any likely clinical history mistakes. Similarly, the patients will not need to stress over having a second assessment from another specialist, because of the straightforwardness of the information. Having patient records on a blockchain organization will prompt individuals to know and associate with various others, across the globe, with similar ailments as they have, which is not only valuable for their well-being, but also make the patients feel acknowledged, upheld, and have reinforced determination to battle the ailment. Patients will have total independence regarding their information, and they will choose who to impart the information to. In this paper, we present all the challenges and critical issues associated with implementing blockchains in the healthcare sector

A Smart IoT-Based Prototype System for Rehabilitation Monitoring

Smart healthcare is growing significantly in the healthcare sector due to the Internet of Things. A remote monitoring system is one of the smart healthcare implementations for rehabilitating stroke patients. Nowadays, as the COVID-19 pandemic continues to spread, patients undergoing home rehabilitation have difficulty meeting with their physicians due to movement constraints. In addition, the healthcare facilities are devoted to treating patients with COVID-19. As a result, physicians and patients could not frequently meet to gather their rehabilitation progress. This study involves developing a prototype to monitor a post-stroke patient's rehabilitation process using the Arduino Nano 33 Bluetooth Low Energy (BLE) and force-sensing resistor (FSR). The prototype analyzes critical aspects of the rehabilitation process based on handgrip, heart rate, sleep, and step tracking measurements. The results of the handgrip, heart rate, sleep, and step tracking measurements are evaluated for various types of subjects and six testing approaches showed an accurate and consistent results. However, experiments partially success with a small error is detected while tracking the steps of each subject. Several recommendations are highlighted to improve the prototype using other sensors such as force sensing resistor and flex sensor for handgrip force transducer, electromyogram (EMG) sensor for stroke-patients rehabilitation, and others

A Smart IoT-Based Prototype System for Rehabilitation Monitoring

Smart healthcare is growing significantly in the healthcare sector due to the Internet of Things. A remote monitoring system is one of the smart healthcare implementations for rehabilitating stroke patients. Nowadays, as the COVID-19 pandemic continues to spread, patients undergoing home rehabilitation have difficulty meeting with their physicians due to movement constraints. In addition, the healthcare facilities are devoted to treating patients with COVID-19. As a result, physicians and patients could not frequently meet to gather their rehabilitation progress. This study involves developing a prototype to monitor a post-stroke patient's rehabilitation process using the Arduino Nano 33 Bluetooth Low Energy (BLE) and force-sensing resistor (FSR). The prototype analyzes critical aspects of the rehabilitation process based on handgrip, heart rate, sleep, and step tracking measurements. The results of the handgrip, heart rate, sleep, and step tracking measurements are evaluated for various types of subjects and six testing approaches showed an accurate and consistent results. However, experiments partially success with a small error is detected while tracking the steps of each subject. Several recommendations are highlighted to improve the prototype using other sensors such as force sensing resistor and flex sensor for handgrip force transducer, electromyogram (EMG) sensor for stroke-patients rehabilitation, and others